Overview

Dataset statistics

Number of variables13
Number of observations358
Missing cells624
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.2 KiB
Average record size in memory109.4 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author성북구
URLhttps://data.seoul.go.kr/dataList/OA-11125/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
업종코드 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 업종코드High correlation
인허가번호 is highly overall correlated with 업종코드High correlation
업종코드 is highly imbalanced (58.4%)Imbalance
지도점검구분 is highly imbalanced (62.2%)Imbalance
처분대상여부 is highly imbalanced (85.0%)Imbalance
처분대상여부 has 32 (8.9%) missing valuesMissing
점검결과 has 358 (100.0%) missing valuesMissing
소재지도로명주소 has 205 (57.3%) missing valuesMissing
소재지주소 has 28 (7.8%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 00:39:53.499571
Analysis finished2024-05-11 00:39:59.719054
Duration6.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct124
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-11T00:40:00.271372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.8687151
Min length3

Characters and Unicode

Total characters2817
Distinct characters199
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)13.1%

Sample

1st row삼덕세차장
2nd row그린에너지-주 종암주유소
3rd row월곡윤활유급유소
4th row금호카세차장
5th row성북주유소
ValueCountFrequency (%)
도원교통(주 18
 
4.5%
고려대학교 15
 
3.8%
대진여객(주 13
 
3.3%
금호카세차장 10
 
2.5%
상진운수(주 9
 
2.3%
주)아띠모 7
 
1.8%
한국과학기술연구원 7
 
1.8%
성진운수(주 6
 
1.5%
영광교통(주 6
 
1.5%
대륙여객자동차(주 6
 
1.5%
Other values (124) 302
75.7%
2024-05-11T00:40:01.700763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 189
 
6.7%
( 123
 
4.4%
) 123
 
4.4%
차 102
 
3.6%
대 94
 
3.3%
유 80
 
2.8%
장 73
 
2.6%
원 71
 
2.5%
세 71
 
2.5%
소 69
 
2.4%
Other values (189) 1822
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2484
88.2%
Open Punctuation 123
 
4.4%
Close Punctuation 123
 
4.4%
Space Separator 41
 
1.5%
Decimal Number 18
 
0.6%
Uppercase Letter 14
 
0.5%
Dash Punctuation 11
 
0.4%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 189
 
7.6%
차 102
 
4.1%
대 94
 
3.8%
유 80
 
3.2%
장 73
 
2.9%
원 71
 
2.9%
세 71
 
2.9%
소 69
 
2.8%
교 64
 
2.6%
학 64
 
2.6%
Other values (177) 1607
64.7%
Decimal Number
ValueCountFrequency (%)
1 8
44.4%
2 5
27.8%
4 5
27.8%
Uppercase Letter
ValueCountFrequency (%)
S 7
50.0%
K 6
42.9%
G 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
. 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2484
88.2%
Common 319
 
11.3%
Latin 14
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 189
 
7.6%
차 102
 
4.1%
대 94
 
3.8%
유 80
 
3.2%
장 73
 
2.9%
원 71
 
2.9%
세 71
 
2.9%
소 69
 
2.8%
교 64
 
2.6%
학 64
 
2.6%
Other values (177) 1607
64.7%
Common
ValueCountFrequency (%)
( 123
38.6%
) 123
38.6%
41
 
12.9%
- 11
 
3.4%
1 8
 
2.5%
2 5
 
1.6%
4 5
 
1.6%
, 2
 
0.6%
. 1
 
0.3%
Latin
ValueCountFrequency (%)
S 7
50.0%
K 6
42.9%
G 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2484
88.2%
ASCII 333
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 189
 
7.6%
차 102
 
4.1%
대 94
 
3.8%
유 80
 
3.2%
장 73
 
2.9%
원 71
 
2.9%
세 71
 
2.9%
소 69
 
2.8%
교 64
 
2.6%
학 64
 
2.6%
Other values (177) 1607
64.7%
ASCII
ValueCountFrequency (%)
( 123
36.9%
) 123
36.9%
41
 
12.3%
- 11
 
3.3%
1 8
 
2.4%
S 7
 
2.1%
K 6
 
1.8%
2 5
 
1.5%
4 5
 
1.5%
, 2
 
0.6%
Other values (2) 2
 
0.6%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0700002 × 1017
Minimum3.0700002 × 1017
Maximum3.0700006 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-11T00:40:02.262748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0700002 × 1017
5-th percentile3.0700002 × 1017
Q13.0700002 × 1017
median3.0700002 × 1017
Q33.0700002 × 1017
95-th percentile3.0700002 × 1017
Maximum3.0700006 × 1017
Range4.00013 × 1010
Interquartile range (IQR)200064

Descriptive statistics

Standard deviation2.1962002 × 109
Coefficient of variation (CV)7.153746 × 10-9
Kurtosis283.32052
Mean3.0700002 × 1017
Median Absolute Deviation (MAD)20160
Skewness16.242582
Sum-7.744565 × 1017
Variance4.8232952 × 1018
MonotonicityNot monotonic
2024-05-11T00:40:02.895680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307000022200000013 11
 
3.1%
307000022200020213 10
 
2.8%
307000022200500003 8
 
2.2%
307000022200900002 7
 
2.0%
307000021200000008 7
 
2.0%
307000022200000060 6
 
1.7%
307000022200000058 6
 
1.7%
307000022200000066 6
 
1.7%
307000022200000021 6
 
1.7%
307000022200000008 6
 
1.7%
Other values (118) 285
79.6%
ValueCountFrequency (%)
307000021200000004 4
1.1%
307000021200000006 5
1.4%
307000021200000007 3
0.8%
307000021200000008 7
2.0%
307000021200000009 4
1.1%
307000021200000016 1
 
0.3%
307000021200300012 4
1.1%
307000021201300001 4
1.1%
307000021201500002 1
 
0.3%
307000021201500003 1
 
0.3%
ValueCountFrequency (%)
307000061201300001 1
 
0.3%
307000034200000022 1
 
0.3%
307000022201700001 1
 
0.3%
307000022201500001 1
 
0.3%
307000022201400001 3
0.8%
307000022201100006 1
 
0.3%
307000022201100005 2
0.6%
307000022201100004 4
1.1%
307000022201100003 3
0.8%
307000022201100002 4
1.1%

업종코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
22
300 
21
57 
34
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 300
83.8%
21 57
 
15.9%
34 1
 
0.3%

Length

2024-05-11T00:40:03.558102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:40:03.966849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 300
83.8%
21 57
 
15.9%
34 1
 
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
폐수배출업소관리
274 
대기배출업소관리
55 
<NA>
29 

Length

Max length8
Median length8
Mean length7.6759777
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐수배출업소관리
2nd row폐수배출업소관리
3rd row폐수배출업소관리
4th row폐수배출업소관리
5th row폐수배출업소관리

Common Values

ValueCountFrequency (%)
폐수배출업소관리 274
76.5%
대기배출업소관리 55
 
15.4%
<NA> 29
 
8.1%

Length

2024-05-11T00:40:04.958023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:40:05.413268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐수배출업소관리 274
76.5%
대기배출업소관리 55
 
15.4%
na 29
 
8.1%

지도점검일자
Real number (ℝ)

Distinct150
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136156
Minimum20100414
Maximum20170531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-11T00:40:05.818147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100414
5-th percentile20100779
Q120120312
median20140521
Q320151020
95-th percentile20170337
Maximum20170531
Range70117
Interquartile range (IQR)30708.25

Descriptive statistics

Standard deviation21365.036
Coefficient of variation (CV)0.0010610285
Kurtosis-1.2672866
Mean20136156
Median Absolute Deviation (MAD)19991
Skewness-0.14874773
Sum7.2087439 × 109
Variance4.5646477 × 108
MonotonicityDecreasing
2024-05-11T00:40:06.490665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150701 12
 
3.4%
20120801 6
 
1.7%
20150529 6
 
1.7%
20170525 5
 
1.4%
20170523 5
 
1.4%
20170413 5
 
1.4%
20120110 5
 
1.4%
20161005 5
 
1.4%
20150327 4
 
1.1%
20140806 4
 
1.1%
Other values (140) 301
84.1%
ValueCountFrequency (%)
20100414 4
1.1%
20100513 3
0.8%
20100610 3
0.8%
20100618 2
0.6%
20100625 3
0.8%
20100630 3
0.8%
20100805 3
0.8%
20100825 2
0.6%
20100901 2
0.6%
20100930 3
0.8%
ValueCountFrequency (%)
20170531 1
 
0.3%
20170525 5
1.4%
20170523 5
1.4%
20170427 2
 
0.6%
20170413 5
1.4%
20170324 1
 
0.3%
20161220 4
1.1%
20161219 4
1.1%
20161216 4
1.1%
20161214 4
1.1%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
3070000
358 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3070000
2nd row3070000
3rd row3070000
4th row3070000
5th row3070000

Common Values

ValueCountFrequency (%)
3070000 358
100.0%

Length

2024-05-11T00:40:06.953288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:40:07.280645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3070000 358
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
서울특별시 성북구
358 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 성북구
2nd row서울특별시 성북구
3rd row서울특별시 성북구
4th row서울특별시 성북구
5th row서울특별시 성북구

Common Values

ValueCountFrequency (%)
서울특별시 성북구 358
100.0%

Length

2024-05-11T00:40:07.685949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:40:08.007858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 358
50.0%
성북구 358
50.0%

지도점검구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
정기
300 
수시
50 
기타
 
4
합동
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row정기

Common Values

ValueCountFrequency (%)
정기 300
83.8%
수시 50
 
14.0%
기타 4
 
1.1%
합동 4
 
1.1%

Length

2024-05-11T00:40:08.412335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:40:08.799506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 300
83.8%
수시 50
 
14.0%
기타 4
 
1.1%
합동 4
 
1.1%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.6%
Missing32
Missing (%)8.9%
Memory size848.0 B
False
319 
True
 
7
(Missing)
32 
ValueCountFrequency (%)
False 319
89.1%
True 7
 
2.0%
(Missing) 32
 
8.9%
2024-05-11T00:40:09.202098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

Distinct93
Distinct (%)26.1%
Missing1
Missing (%)0.3%
Memory size2.9 KiB
2024-05-11T00:40:09.657423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length40
Mean length19.795518
Min length7

Characters and Unicode

Total characters7067
Distinct characters113
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)12.3%

Sample

1st row배출시설 및 방지시설 적정운영 여부
2nd row배출시설 및 방지시설 적정운영 여부
3rd row배출시설 및 방지시설 적정운영여부
4th row배출시설 및 방지시설 적정운영 여부
5th row배출시설 및 방지시설 적정운영 여부
ValueCountFrequency (%)
방지시설 275
16.0%
및 274
15.9%
여부 178
 
10.3%
폐수배출시설 125
 
7.3%
등 92
 
5.3%
배출시설 81
 
4.7%
정상가동 66
 
3.8%
적정운영 57
 
3.3%
적정 45
 
2.6%
대기배출시설 42
 
2.4%
Other values (102) 489
28.4%
2024-05-11T00:40:10.871762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1367
19.3%
설 577
 
8.2%
시 564
 
8.0%
출 323
 
4.6%
배 320
 
4.5%
부 315
 
4.5%
여 311
 
4.4%
지 309
 
4.4%
방 306
 
4.3%
정 306
 
4.3%
Other values (103) 2369
33.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5663
80.1%
Space Separator 1367
 
19.3%
Other Punctuation 28
 
0.4%
Decimal Number 4
 
0.1%
Dash Punctuation 3
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 577
 
10.2%
시 564
 
10.0%
출 323
 
5.7%
배 320
 
5.7%
부 315
 
5.6%
여 311
 
5.5%
지 309
 
5.5%
방 306
 
5.4%
정 306
 
5.4%
및 300
 
5.3%
Other values (94) 2032
35.9%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 25
89.3%
. 3
 
10.7%
Space Separator
ValueCountFrequency (%)
1367
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5663
80.1%
Common 1404
 
19.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 577
 
10.2%
시 564
 
10.0%
출 323
 
5.7%
배 320
 
5.7%
부 315
 
5.6%
여 311
 
5.5%
지 309
 
5.5%
방 306
 
5.4%
정 306
 
5.4%
및 300
 
5.3%
Other values (94) 2032
35.9%
Common
ValueCountFrequency (%)
1367
97.4%
, 25
 
1.8%
. 3
 
0.2%
- 3
 
0.2%
2 2
 
0.1%
) 1
 
0.1%
( 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5662
80.1%
ASCII 1404
 
19.9%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1367
97.4%
, 25
 
1.8%
. 3
 
0.2%
- 3
 
0.2%
2 2
 
0.1%
) 1
 
0.1%
( 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%
Hangul
ValueCountFrequency (%)
설 577
 
10.2%
시 564
 
10.0%
출 323
 
5.7%
배 320
 
5.7%
부 315
 
5.6%
여 311
 
5.5%
지 309
 
5.5%
방 306
 
5.4%
정 306
 
5.4%
및 300
 
5.3%
Other values (93) 2031
35.9%
Compat Jamo
ValueCountFrequency (%)
ㅈ 1
100.0%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing358
Missing (%)100.0%
Memory size3.3 KiB

소재지도로명주소
Text

MISSING 

Distinct83
Distinct (%)54.2%
Missing205
Missing (%)57.3%
Memory size2.9 KiB
2024-05-11T00:40:11.438154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length39
Mean length26.666667
Min length22

Characters and Unicode

Total characters4080
Distinct characters134
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)30.1%

Sample

1st row서울특별시 성북구 종암로 145 (종암동)
2nd row서울특별시 성북구 화랑로 160 (상월곡동)
3rd row서울특별시 성북구 화랑로 118 (하월곡동)
4th row서울특별시 성북구 보문로 142 (보문동1가)
5th row서울특별시 성북구 종암로 12 (종암동)
ValueCountFrequency (%)
서울특별시 153
19.1%
성북구 153
19.1%
정릉동 36
 
4.5%
장위동 25
 
3.1%
하월곡동 18
 
2.2%
종암로 18
 
2.2%
화랑로 16
 
2.0%
종암동 15
 
1.9%
정릉로 14
 
1.8%
17 11
 
1.4%
Other values (149) 341
42.6%
2024-05-11T00:40:12.757760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
678
 
16.6%
성 168
 
4.1%
북 167
 
4.1%
동 166
 
4.1%
) 156
 
3.8%
( 156
 
3.8%
구 155
 
3.8%
서 155
 
3.8%
시 154
 
3.8%
로 153
 
3.8%
Other values (124) 1972
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2534
62.1%
Space Separator 678
 
16.6%
Decimal Number 505
 
12.4%
Close Punctuation 156
 
3.8%
Open Punctuation 156
 
3.8%
Other Punctuation 37
 
0.9%
Dash Punctuation 14
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 168
 
6.6%
북 167
 
6.6%
동 166
 
6.6%
구 155
 
6.1%
서 155
 
6.1%
시 154
 
6.1%
로 153
 
6.0%
별 153
 
6.0%
특 153
 
6.0%
울 153
 
6.0%
Other values (109) 957
37.8%
Decimal Number
ValueCountFrequency (%)
1 118
23.4%
2 71
14.1%
7 61
12.1%
6 47
 
9.3%
5 41
 
8.1%
4 40
 
7.9%
0 38
 
7.5%
3 33
 
6.5%
8 32
 
6.3%
9 24
 
4.8%
Space Separator
ValueCountFrequency (%)
678
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Open Punctuation
ValueCountFrequency (%)
( 156
100.0%
Other Punctuation
ValueCountFrequency (%)
, 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2534
62.1%
Common 1546
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 168
 
6.6%
북 167
 
6.6%
동 166
 
6.6%
구 155
 
6.1%
서 155
 
6.1%
시 154
 
6.1%
로 153
 
6.0%
별 153
 
6.0%
특 153
 
6.0%
울 153
 
6.0%
Other values (109) 957
37.8%
Common
ValueCountFrequency (%)
678
43.9%
) 156
 
10.1%
( 156
 
10.1%
1 118
 
7.6%
2 71
 
4.6%
7 61
 
3.9%
6 47
 
3.0%
5 41
 
2.7%
4 40
 
2.6%
0 38
 
2.5%
Other values (5) 140
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2534
62.1%
ASCII 1546
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
678
43.9%
) 156
 
10.1%
( 156
 
10.1%
1 118
 
7.6%
2 71
 
4.6%
7 61
 
3.9%
6 47
 
3.0%
5 41
 
2.7%
4 40
 
2.6%
0 38
 
2.5%
Other values (5) 140
 
9.1%
Hangul
ValueCountFrequency (%)
성 168
 
6.6%
북 167
 
6.6%
동 166
 
6.6%
구 155
 
6.1%
서 155
 
6.1%
시 154
 
6.1%
로 153
 
6.0%
별 153
 
6.0%
특 153
 
6.0%
울 153
 
6.0%
Other values (109) 957
37.8%

소재지주소
Text

MISSING 

Distinct86
Distinct (%)26.1%
Missing28
Missing (%)7.8%
Memory size2.9 KiB
2024-05-11T00:40:13.567638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length31
Mean length21.009091
Min length14

Characters and Unicode

Total characters6933
Distinct characters95
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)5.5%

Sample

1st row서울특별시 성북구 정릉동 45-12번지
2nd row서울특별시 성북구 종암동 3-545번지
3rd row서울특별시 성북구 상월곡동 52-2번지
4th row서울특별시 성북구 하월곡동 13-26번지
5th row서울특별시 성북구 보문동1가 9-3번지
ValueCountFrequency (%)
서울특별시 330
26.2%
성북구 327
26.0%
정릉동 88
 
7.0%
하월곡동 51
 
4.1%
장위동 45
 
3.6%
안암동5가 27
 
2.1%
종암동 26
 
2.1%
석관동 24
 
1.9%
상월곡동 11
 
0.9%
13-26번지 10
 
0.8%
Other values (103) 319
25.4%
2024-05-11T00:40:14.848166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1259
18.2%
성 341
 
4.9%
동 340
 
4.9%
시 338
 
4.9%
북 335
 
4.8%
서 333
 
4.8%
특 330
 
4.8%
별 330
 
4.8%
구 330
 
4.8%
울 330
 
4.8%
Other values (85) 2667
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4443
64.1%
Space Separator 1259
 
18.2%
Decimal Number 999
 
14.4%
Dash Punctuation 205
 
3.0%
Other Punctuation 17
 
0.2%
Open Punctuation 5
 
0.1%
Close Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 341
 
7.7%
동 340
 
7.7%
시 338
 
7.6%
북 335
 
7.5%
서 333
 
7.5%
특 330
 
7.4%
별 330
 
7.4%
구 330
 
7.4%
울 330
 
7.4%
지 223
 
5.0%
Other values (70) 1213
27.3%
Decimal Number
ValueCountFrequency (%)
1 260
26.0%
2 149
14.9%
3 101
 
10.1%
5 94
 
9.4%
8 91
 
9.1%
0 78
 
7.8%
4 77
 
7.7%
6 57
 
5.7%
7 54
 
5.4%
9 38
 
3.8%
Space Separator
ValueCountFrequency (%)
1259
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 205
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4443
64.1%
Common 2490
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 341
 
7.7%
동 340
 
7.7%
시 338
 
7.6%
북 335
 
7.5%
서 333
 
7.5%
특 330
 
7.4%
별 330
 
7.4%
구 330
 
7.4%
울 330
 
7.4%
지 223
 
5.0%
Other values (70) 1213
27.3%
Common
ValueCountFrequency (%)
1259
50.6%
1 260
 
10.4%
- 205
 
8.2%
2 149
 
6.0%
3 101
 
4.1%
5 94
 
3.8%
8 91
 
3.7%
0 78
 
3.1%
4 77
 
3.1%
6 57
 
2.3%
Other values (5) 119
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4443
64.1%
ASCII 2490
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1259
50.6%
1 260
 
10.4%
- 205
 
8.2%
2 149
 
6.0%
3 101
 
4.1%
5 94
 
3.8%
8 91
 
3.7%
0 78
 
3.1%
4 77
 
3.1%
6 57
 
2.3%
Other values (5) 119
 
4.8%
Hangul
ValueCountFrequency (%)
성 341
 
7.7%
동 340
 
7.7%
시 338
 
7.6%
북 335
 
7.5%
서 333
 
7.5%
특 330
 
7.4%
별 330
 
7.4%
구 330
 
7.4%
울 330
 
7.4%
지 223
 
5.0%
Other values (70) 1213
27.3%

Interactions

2024-05-11T00:39:56.793662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:39:56.072142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:39:57.221883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:39:56.467490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:40:15.431550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부점검사항소재지도로명주소소재지주소
인허가번호1.0000.9460.0740.2470.0980.2701.000NaN1.000
업종코드0.9461.0001.0000.4080.0730.0000.9991.0000.974
업종명0.0741.0001.0000.5490.0000.0000.9901.0000.829
지도점검일자0.2470.4080.5491.0000.3540.0000.9860.0000.457
지도점검구분0.0980.0730.0000.3541.0000.2700.8700.3770.529
처분대상여부0.2700.0000.0000.0000.2701.0000.6530.0000.541
점검사항1.0000.9990.9900.9860.8700.6531.0000.0000.783
소재지도로명주소NaN1.0001.0000.0000.3770.0000.0001.0001.000
소재지주소1.0000.9740.8290.4570.5290.5410.7831.0001.000
2024-05-11T00:40:15.944689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종코드처분대상여부지도점검구분업종명
업종코드1.0000.0000.0680.989
처분대상여부0.0001.0000.1790.000
지도점검구분0.0680.1791.0000.000
업종명0.9890.0000.0001.000
2024-05-11T00:40:16.450499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.1130.7100.0000.0950.175
지도점검일자-0.1131.0000.2810.4080.2590.000
업종코드0.7100.2811.0000.9890.0680.000
업종명0.0000.4080.9891.0000.0000.000
지도점검구분0.0950.2590.0680.0001.0000.179
처분대상여부0.1750.0000.0000.0000.1791.000

Missing values

2024-05-11T00:39:57.991130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:39:58.876568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-11T00:39:59.483459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
0삼덕세차장30700002220020005222폐수배출업소관리201705313070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA><NA>서울특별시 성북구 정릉동 45-12번지
1그린에너지-주 종암주유소30700002220000004522폐수배출업소관리201705253070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 종암로 145 (종암동)서울특별시 성북구 종암동 3-545번지
2월곡윤활유급유소30700002220000002122폐수배출업소관리201705253070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영여부<NA>서울특별시 성북구 화랑로 160 (상월곡동)서울특별시 성북구 상월곡동 52-2번지
3금호카세차장30700002220002021322폐수배출업소관리201705253070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 화랑로 118 (하월곡동)서울특별시 성북구 하월곡동 13-26번지
4성북주유소30700002220000004422폐수배출업소관리201705253070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 보문로 142 (보문동1가)서울특별시 성북구 보문동1가 9-3번지
5현대오일뱅크(주)직영고대주유소30700002220000004722폐수배출업소관리201705253070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 종암로 12 (종암동)서울특별시 성북구 종암동 30-24번지
6북악주유소30700002220000007122폐수배출업소관리201705233070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 정릉로 218 (정릉동)서울특별시 성북구 정릉동 428번지
7주-원천석유 원천제1주유소30700002220000003122폐수배출업소관리201705233070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 보국문로 52 (정릉동)서울특별시 성북구 정릉동 401-15번지
8현대세차장30700002220000007322폐수배출업소관리201705233070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 성북구 정릉로 255 (정릉동)서울특별시 성북구 정릉동 161-13번지
9삼선주유소30700002220000000722폐수배출업소관리201705233070000서울특별시 성북구정기N배출시설 및 방지시설 적정운영 여부<NA><NA>서울특별시 성북구 삼선동1가
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
348영도상운(주)30700002220012021722폐수배출업소관리201006103070000서울특별시 성북구정기N폐수배출시설 및 방지시설 적정가동 여부 등<NA><NA>서울특별시 성북구 하월곡동 67-50번지
349을지운수(주)30700002220000005822폐수배출업소관리201006103070000서울특별시 성북구정기N폐수배출시설 및 방지시설 적정가동 여부 등<NA><NA>서울특별시 성북구 정릉동
350영광교통(주)30700002220000005322폐수배출업소관리201006103070000서울특별시 성북구정기N폐수배출시설 및 방지시설 적정 가동 여부 등<NA><NA>서울특별시 성북구 장위동
351화랑로주유소30700002220000005522폐수배출업소관리201005133070000서울특별시 성북구정기N폐수배출시설 및 방지시설 적정 가동여부 등<NA><NA>서울특별시 성북구 상월곡동 23-11번지 화랑로주유소
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